I began applying for Information Technology jobs on August 11th, 2020. I used LinkedIn as my primary source of information. I varied between large and small companies, EasyApply and normal applications via company job application portals or website submissions, and various locations across the US. I even applied to a job in Sydney, Australia (by accident) and a job in Toronto, Canada.
I took a break from 2020.11.06 - 2020.12.17.
I saved my application information in an Excel Spreadsheet as I applied. I decided to use it to practice some visualization techniques.
#General Packages.
from datetime import date
from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import warnings
warnings.filterwarnings('ignore')
#Specific Packages
from wordcloud import WordCloud
import bar_chart_race as bcr
from IPython.display import Video
import plotly.graph_objects as go
from pywaffle import Waffle
#Read data.
df = pd.read_excel(r'C:\Users\Rohan\Documents\Data Science\Completed\Jobs\Jobs.xlsx')
lat_lons = pd.read_excel(r'C:\Users\Rohan\Documents\Data Science\Completed\Jobs\uscities.xlsx')
#Convert datetime columns to date.
df['Date_Applied'] = df['Date_Applied'].apply(lambda x: x.date())
df['Rejection_Email'] = df['Rejection_Email'].apply(lambda x: x.date())
df['Viewed_Email'] = df['Viewed_Email'].apply(lambda x: x.date())
#Current Numbers
print("As of " + date.today().strftime("%A, %B %d, %Y") + ", I have applied to " + str(df.shape[0]) + " jobs.")
As of Tuesday, February 16, 2021, I have applied to 976 jobs.
This section looks at the specific job titles.
Below are the top 10 and bottom 10 (some of the single occuring) words from the job titles.
#Combine all job titles into one string.
jobs_string = ' '.join(df['Title'])
#Only letters are useful.
regex = re.compile('[^a-zA-Z]')
#Remove all non letters, and remove ' I ', ' II ', ' III '.
jobs_string = regex.sub(' ', jobs_string).replace(' I ',' ').replace(' II ',' ').replace(' III ',' ')
#Specific Replacement.
jobs_string = jobs_string.replace('AR VR','AR/VR').replace('C C', 'C2C').replace('Microsoft', 'Microsoft-365').replace('Non IT', 'Non-IT')
#Create a frequency distribution of all words in the job titles.
jobs_dict = {}
jobs_words = jobs_string.split()
for word in jobs_words :
if word not in jobs_dict.keys() :
jobs_dict[word] = 1
else :
jobs_dict[word] += 1
#Convert frequency distribution to dataframe, sort by frequency.
jobs_df = pd.DataFrame({'Word' : list(jobs_dict.keys()),
'Count' : list(jobs_dict.values())}).sort_values(by = 'Count', ascending = False, axis = 0).reset_index(drop = True)
jobs_df.head(10).transpose()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Word | Data | Scientist | Analyst | Engineer | Machine | Learning | Python | Developer | Analytics | Junior |
| Count | 884 | 474 | 312 | 141 | 64 | 63 | 57 | 46 | 36 | 27 |
jobs_df.tail(10).transpose()
| 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Word | Banking | Model | w | Commercial | experience | Platform | Web | Functional | Global | Statistician |
| Count | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
I thought a word cloud would be a fun representation to look at the job titles. The proportional size has been rescaled.
jobs_wc = WordCloud(background_color = 'lightgrey',
max_words = 300,
collocations = False,
relative_scaling = 0)
jobs_wc.generate(jobs_string)
plt.figure(figsize = (15, 11))
plt.imshow(jobs_wc, interpolation = 'bilinear')
plt.axis('off');
Some companies are hiring heavily. Some are recruiting and staffing agencies for others.
Once my application was in a system on a particular company's career portal page, it was easy to reapply. I used this quite a bit for companies like Amazon, Google, MITRE, and PayPal.
I used LinkedIn's EasyApply for many applications as well.
For the others, I sometimes applied as a guest, sometimes only had to upload my resume and cover, sometimes had to go through a 20 minute ordeal just for one opening. It varied. ¯_( ͡° ͜ʖ ͡°)_/¯ .
See Appendix for full table of cumulative applications by company and date, sorted alphabetically and chronologically, respectively.
#List of all companies.
companies = df['Company'].unique()
#Dates from first application to today.
date_index = pd.date_range(start = min(df['Date_Applied']), end = date.today())
#Create new data frame of 0s.
application_df = pd.DataFrame(index = date_index, columns = companies).fillna(0)
#Create cumulative count of job applications by company and date.
for i in range(len(df)) :
company = df.iloc[i, 1]
date_app = df.iloc[i, 7]
application_df.loc[date_app:, company] += 1
#Alphabetical
application_df = application_df.reindex(sorted(application_df.columns), axis=1)
#Total number of applications to each company.
cumulative_app_count = application_df.iloc[[-1]]
major_companies = cumulative_app_count.columns[(cumulative_app_count >= 6).iloc[0]]
minor_companies = cumulative_app_count.columns[(cumulative_app_count < 6).iloc[0]].tolist()
#Create a dummy company called 'Other', containing all companies with less than 6 applications.
major_df = pd.DataFrame.copy(application_df)
major_df['Other'] = major_df[minor_companies].sum(axis = 1)
major_df.drop(minor_companies, axis = 1, inplace = True)
#Shape check.
major_df.shape
#pd.set_option("display.max_rows", None, "display.max_columns", None)
#major_df.sum(axis=1)
(190, 34)
Below is an animation of the above data. Bar chart races run more smoothly with larger numbers, like population, or monetary amounts, over longer periods of time. But I am happy the way this turned out.
bold_colors = ['#f0f0f0', '#3cb44b', '#e6194b', '#fffac8', '#9a6324', '#e6beff', '#fabebe', '#000075',
'#ffe119', '#008080', '#4363d8', '#ffffff', '#bcf60c', '#46f0f0', '#911eb4', '#800000',
'#f032e6', '#808000', '#ffd8b1', '#f58231', '#aaffc3', '#ca1699', '#800080', '#000000']
apps = bcr.bar_chart_race(df = major_df,
filename = 'applications.mp4',
orientation = 'h',
sort = 'desc',
cmap = bold_colors,
filter_column_colors = True,
period_fmt = '%B %d, %Y',
period_label = {'x': 0.99,
'y': 0.26,
'ha': 'right',
'size': 14},
period_summary_func = lambda v, r: {'x': 0.99,
'y': 0.05,
's': f"{v.sum():,.0f} applications completed.\n\nCompanies with less than 6 applications are grouped in 'Other'.",
'ha': 'right',
'size': 9,
'weight': 'normal'},
steps_per_period = 20,
period_length = 250,
title = 'Total Number of Jobs Applied to by Company and Date')
Video("applications.mp4")
Some slight modifications were made from LinkedIn data during the application process:
In addition, for Common App, the job location was changed from none specified to Arlington. I did not learn about their opening from LinkedIn.
Several jobs were advertised with no city, only remote.
df[df['City'] == "Remote"]
| Title | Company | Size | City | State_abbv | State | Date_Posted | Date_Applied | Rejection_Email | Viewed_Email | CoID | JobID | URL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 195 | Data Scientist | Pilot Flying J | 10001+ | Remote | NaN | NaN | 2020-09-04 00:00:00 | 2020-09-06 | 2020-09-17 | NaT | NaN | 2.019452e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 419 | Forward Deployed Data Scientist | Cresta | 11-50 | Remote | NaN | NaN | 2020-09-29 00:00:00 | 2020-09-30 | NaT | NaT | NaN | 2.155368e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 420 | Data Scientist | White Ops | 51-200 | Remote | NaN | NaN | > 1 week | 2020-09-30 | 2020-10-15 | NaT | NaN | 2.023661e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 433 | Intern - AI/Machine Learning | Seagate Technology | 10001+ | Remote | OR | Oregon | 2020-09-30 00:00:00 | 2020-10-01 | NaT | NaT | 201783 | 2.183372e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 437 | Platform Data Engineer | Demyst | 51-200 | Remote | NaN | NaN | 2020-10-01 00:00:00 | 2020-10-01 | NaT | NaT | NaN | 2.152177e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 522 | Data Scientist | Possible | 11-50 | Remote | NaN | NaN | 2020-10-14 00:00:00 | 2020-10-15 | NaT | 2020-10-16 | NaN | 2.183079e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 588 | Data Scientist | Imperfect Foods | 1001-5000 | Remote | NaN | NaN | 2020-12-16 00:00:00 | 2020-12-23 | NaT | NaT | NaN | 2.306004e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 639 | Data Analyst | Concentric | 11-50 | Remote | NaN | NaN | 2020-12-31 00:00:00 | 2020-12-31 | NaT | NaT | NaN | 2.363471e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 662 | Data Engineer | Peterson Technology Partners | 201-500 | Remote | NaN | NaN | 2020-12-31 00:00:00 | 2021-01-02 | NaT | NaT | NaN | 2.332354e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 711 | Quantitative Analyst | Silverpips | 2-10 | Remote | NaN | NaN | 2021-01-09 00:00:00 | 2021-01-09 | NaT | NaT | NaN | 2.374559e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 744 | Python Developer/ML/Data Engineer | X-Team | NaN | Remote | NaN | NaN | >1 week | 2021-01-11 | 2021-01-14 | 2021-01-12 | NaN | NaN | https://www.linkedin.com/jobs/search/?currentJ... |
| 824 | Data Analyst / SQL Developer | Modis | 10001 | Remote | MI | Michigan | 2021-01-20 00:00:00 | 2021-01-21 | NaT | NaT | NaN | 2.367098e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 838 | Data Scientist | Yelp | 1001-5000 | Remote | OR | Oregon | 2021-01-22 00:00:00 | 2021-01-23 | NaT | NaT | NaN | 2.377888e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 938 | Junior Data Analyst | Abzooba | 51-200 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | NaT | NaN | 2.420192e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 941 | Data Scientist | Pixalate | 51-200 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | NaT | NaN | 2.420264e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 945 | Python Developer | Kavaliro | 501-1000 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | NaT | NaN | 2.419931e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 956 | Data Engineer / Data Scientist | Advantage Tech | 51-200 | Remote | NaN | NaN | 2021-02-10 00:00:00 | 2021-02-15 | NaT | NaT | NaN | 2.416364e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
I retrieved their office locations and manually entered them.
#Manual City, State_abbv, and State entry.
dict_cs = {195: ("Knoxville", "TN", "Tennessee"),
419: ("San Francisco", "CA", "California"),
420: ("New York", "NY", "New York"),
433: ("New York", "NY", "New York"),
437: ("Portland", "OR", "Oregon"),
522: ("Seattle", "WA", "Washington"),
588: ("San Francisco", "CA", "California"),
639: ("Cambridge", "MA", "Massachusetts"),
662: ("Park Ridge", "IL", "Illinois"),
711: ("Wilmington", "DE", "Delaware"),
744: ("Melbourne", "AU", "Australia"),
824: ("Southfield", "MI", "Michigan"),
838: ("Portland", "OR", "Oregon"),
938: ("Milpitas", "CA", "California"),
941: ("Palo Alto", "CA", "California"),
945: ("Orlando", "FL", "Florida"),
956: ("Overland Park", "KS", "Kansas")}
#Loop to manually enter missing City, State_abbv, and State entry for Remote locations.
for idx in dict_cs :
temp_city = dict_cs[idx][0]
temp_state_abbv = dict_cs[idx][1]
temp_state = dict_cs[idx][2]
df.at[idx, 'City'] = temp_city
df.at[idx, 'State_abbv'] = temp_state_abbv
df.at[idx, 'State'] = temp_state
Several jobs I applied to were in cities not in the spreadsheet I downloaded.
#Select relevant columns.
df_loc = df[['City', 'State_abbv', 'State', 'Date_Applied']]
#Select only city, state, and location columns.
lat_lons = lat_lons[['city', 'state_id', 'lat', 'lng']]
#Count the number of applications.
city_tally = df_loc.groupby(['City', 'State_abbv', 'State']).count().reset_index()
#Merge to get latitude, longitude for each city.
merged = pd.merge(city_tally,
lat_lons,
how = 'left',
left_on = ['City', 'State_abbv'],
right_on = ['city', 'state_id'])
#Several cities are not in the list.
merged[merged['city'].isna()]
| City | State_abbv | State | Date_Applied | city | state_id | lat | lng | |
|---|---|---|---|---|---|---|---|---|
| 12 | Bedford | MA | Massachusetts | 4 | NaN | NaN | NaN | NaN |
| 26 | Bridgewater | NJ | New Jersey | 2 | NaN | NaN | NaN | NaN |
| 34 | Center Valley | PA | Pennsylvania | 1 | NaN | NaN | NaN | NaN |
| 50 | Concord | MA | Massachusetts | 3 | NaN | NaN | NaN | NaN |
| 53 | Dallas - Ft. Worth | TX | Texas | 16 | NaN | NaN | NaN | NaN |
| 63 | East Hanover | NJ | New Jersey | 2 | NaN | NaN | NaN | NaN |
| 91 | Hunt Valley | MD | Maryland | 1 | NaN | NaN | NaN | NaN |
| 115 | Melbourne | AU | Australia | 1 | NaN | NaN | NaN | NaN |
| 142 | Parsippany | NJ | New Jersey | 1 | NaN | NaN | NaN | NaN |
| 143 | Patuxent River | MD | Maryland | 1 | NaN | NaN | NaN | NaN |
| 171 | Sandy Hook | CT | Connecticut | 1 | NaN | NaN | NaN | NaN |
| 189 | Sydney | AU | Australia | 1 | NaN | NaN | NaN | NaN |
| 193 | Toronto | CN | Canada | 1 | NaN | NaN | NaN | NaN |
| 196 | Utica Rome | NY | New York | 1 | NaN | NaN | NaN | NaN |
| 204 | West Shokan | NY | New York | 2 | NaN | NaN | NaN | NaN |
| 205 | Westchester | CA | California | 1 | NaN | NaN | NaN | NaN |
| 206 | Weston | MA | Massachusetts | 1 | NaN | NaN | NaN | NaN |
Two were from Australia and one from Canada. For the others, I retrieved values from Google. An approximate average value was chosen for Dallas-Ft. Worth.
#Huxley job from Sydney, Australia was removed.
merged = merged[merged.State_abbv != 'AU']
#Prodigy Academy job from Toronto, Canada was removed.
merged = merged[merged.State_abbv != 'CN']
#Manual latitude and longitude entry.
dict_loc = {12: (42.4906, -71.2760),
26: (40.5940, -74.6049),
34: (40.5294, -75.3937),
50: (42.4604, -71.3489),
53: (32.7598, -97.0646),
63: (40.8201, -74.3647),
91: (39.4900, -76.6585),
142: (40.8653, -74.4174),
143: (38.2773, -76.4229),
171: (41.4218, -73.2833),
196: (43.2763, -75.1545),
204: (41.9671, -74.2871),
205: (33.9626, 118.3988),
206: (42.3668, -71.3031)}
#Loop to manually enter missing latitude and longitudes.
for idx in dict_loc :
lat = dict_loc[idx][0]
lon = dict_loc[idx][1]
merged.at[idx, 'lat'] = lat
merged.at[idx, 'lng'] = lon
#Rename columns and drop redundant columns
merged = merged.rename(columns = {'Date_Applied' : 'Count',
'lat': 'Latitude',
'lng': 'Longitude'}).drop(['city', 'state_id'], axis = 1)
merged.shape
(209, 6)
The left join was off. I soon determined that Chevy Chase, MD was repeated for some reason.
merged[merged.City == 'Chevy Chase']
| City | State_abbv | State | Count | Latitude | Longitude | |
|---|---|---|---|---|---|---|
| 41 | Chevy Chase | MD | Maryland | 1 | 38.9943 | -77.0737 |
| 42 | Chevy Chase | MD | Maryland | 1 | 38.9819 | -77.0833 |
merged = merged[(merged['Latitude'] != 38.9819) | (merged['Longitude'] != -77.0833)]
In addition, two cities in California are named Mountain View.
merged[merged.City == 'Mountain View']
| City | State_abbv | State | Count | Latitude | Longitude | |
|---|---|---|---|---|---|---|
| 127 | Mountain View | CA | California | 17 | 37.4000 | -122.0796 |
| 128 | Mountain View | CA | California | 17 | 38.0093 | -122.1169 |
All cities, with their state, number of applications, and location, are displayed below.
merged = merged[(merged['Latitude'] != 38.0093) | (merged['Longitude'] != -122.1169)]
merged
| City | State_abbv | State | Count | Latitude | Longitude | |
|---|---|---|---|---|---|---|
| 0 | Allentown | PA | Pennsylvania | 1 | 40.5961 | -75.4756 |
| 1 | Alpharetta | GA | Georgia | 3 | 34.0704 | -84.2739 |
| 2 | Anaheim | CA | California | 1 | 33.8390 | -117.8572 |
| 3 | Andover | MA | Massachusetts | 1 | 42.6554 | -71.1418 |
| 4 | Arlington | VA | Virginia | 8 | 38.8786 | -77.1011 |
| ... | ... | ... | ... | ... | ... | ... |
| 207 | Wilmington | DE | Delaware | 3 | 39.7415 | -75.5413 |
| 208 | Winston-Salem | NC | North Carolina | 1 | 36.1029 | -80.2611 |
| 209 | Woodbridge | NJ | New Jersey | 1 | 40.5550 | -74.2846 |
| 210 | Woonsocket | RI | Rhode Island | 2 | 42.0010 | -71.4993 |
| 211 | Yuba City | CA | California | 1 | 39.1357 | -121.6381 |
207 rows × 6 columns
See Appendix for frequency by state.
#Group job applications by State, count them, reset the index, drop the date, and rename City to Count.
state_tally = merged[['Count', 'State_abbv', 'State']].groupby(['State_abbv', 'State']).sum().sort_values(by = 'Count', ascending = False, axis = 0).reset_index()
Below is an interactive map of the US. Applications are sorted by city and state.
#Singular or Plural.
def f(row) :
if row['Count'] == 1 :
string_val = ' application in '
else :
string_val = ' applications in '
return string_val
#Number of applications per city as text.
merged['Text'] = merged['Count'].astype(str) + merged.apply(f, axis = 1) + merged['City'] + ', ' + merged['State_abbv'] + '.'
#Number of applications per state as text.
state_tally['Text'] = state_tally['Count'].astype(str) + state_tally.apply(f, axis = 1) + state_tally['State'] + '.'
#Color in states by number of applications.
state_map_data = go.Choropleth(locations = state_tally['State_abbv'],
z = state_tally['Count'],
text = state_tally['Text'],
hoverinfo = 'text',
locationmode = 'USA-states',
colorbar = {'title': "<b>Applications</b>",
'thicknessmode': "pixels",
'thickness': 70,
'lenmode': "pixels",
'len': 400,
'titlefont': {'size': 16},
'tickfont': {'size': 12},
'tickvals': [60, 120, 180]},
colorscale = 'Blues')
#Plot cities, size corresponds to number of applications.
city_map_data = go.Scattergeo(lon = merged['Longitude'],
lat = merged['Latitude'],
text = merged['Text'],
hoverinfo = 'text',
locationmode = 'USA-states',
marker = {'size': 10*np.sqrt(merged['Count']),
'color': 'Darkgreen'})
data = [state_map_data, city_map_data]
fig = go.Figure(data = data)
fig.update_layout(title = {'text': 'Where I Have Applied (Hover and Zoom)',
'font': {'size': 30}},
geo_scope = 'usa',
width = 950,
height = 550)
Below is the distribution of applications by state.
#Sort by number of applications for each state.
waffle_data = state_tally[['State', 'Count']]
#Add a row for states with less than 3 applications. 'Other' also includes Australia and Canada.
waffle_data = waffle_data.append(pd.DataFrame({'State': ['Other'], 'Count': [3]})).reset_index(drop = True)
to_drop = []
#Add applications from states with less than 5 to 'Other'.
for i in waffle_data.index :
if waffle_data.iloc[i]['Count'] < 5:
temp = waffle_data.iloc[i]['Count']
waffle_data.at[35, 'Count'] += temp
to_drop.append(i)
#Remove states with less than 3. Change orientation of data.
waffle_data = waffle_data.drop(labels = to_drop, axis = 0).reset_index(drop = True).set_index('State').transpose()
print(waffle_data.sum(axis = 1))
print(waffle_data.shape)
Count 976 dtype: int64 (1, 24)
fig = plt.figure(FigureClass = Waffle,
rows = 16,
values = waffle_data.values.tolist()[0],
labels = waffle_data.columns.tolist(),
figsize = (15, 10),
colors = bold_colors[0:24],
legend = {'loc': 'upper left',
'ncol': 2,
'fontsize': 14})
pd.set_option('display.max_columns', None)
display(application_df)
| 310 Nutrition | 777 Partners | AETEA Information Technology | ALTEN | AP Professionals | ARYZTA | Abzooba | Accelere | AdTheorent | Addison Group | Addison Professional Financial Search LLC | Aditi Consulting | Adore Me | Advanced Auto Parts | Advantage Tech | Adwait Algorithm | Affinity Search Partners | Age of Learning | Agility Partners | AimHire | Albertsons Companies | Alcimed | Alexander Technology Group | Allied Universal | Allstacks | Amazon | American Bureau of Shipping | Amtex Enterprises | Analytic Recruiting Inc. | Anson McCade | Apex Systems | Appen | Applied Resource Group | Apptad, Inc. | Aptonet, Inc. | Artisan Talent | Ascii Group, LLC | Associa | Atlantic Broadband | Atlas Reasearch | Austin Fraser | Averity | BCG Digital Ventures | Bayside Solutions | Beacon Hill Staffing Group | Bear Cognition | Bethesda Softworks | Big Cloud | Blastpoint | BlueAlly Services | BombBomb | Bradford & Galt | BrandMuscle | Broadridge | Brooksource | Burtch Works | CBTS | CPS, Inc. | CRISIL Limited | CSI Tech | CVS / Aetna | Calculated Hire | Cancer Expert Now, Inc. | CarMax | CareHarmony | Career Movement | Caserta | Caterpillar | Chicago Connect | CircleUp | Cisco | ClearBridge Technology Group | Clever Devices | Cloud9 Technologies, LLC | Coders Data | Coit Group | Collabera, Inc. | Collage.com | Colwen Hotels | Common App | CompuGain | Compunnel Staffing | Concentric | Constant | ConsultNet | CoreSite | Cornerstone Staffing Solutions, Inc. | Corps Team/Mom Corps | Course Hero | Coursera | Crawford Thomas Recruiting | Cresta | Critical Mass | Crystal Equation Corporation | Curate Partners | CybeCys, Inc. | CyberCoders | Cypress HCM | DAT Solutions | DISYS | DMI | DTS, Inc. | Dahl Consulting | Data+ | DataLab USA | Delphi-US, LLC | Demyst | Dick's Sporting Goods | Diversant, LLC | DoorDash | DrFirst, Inc. | Dstillery | E-Solutions | EPITEC | EZCORP | Edison | Eliassen Group | Elliot Scott HR | Enhance IT | Entech | Entelligence | Envestnet | Yodlee | Envision | EpicTec | Epsilon | Evernote | Expedia | Experis | FICO | FIGS | Fandango | FedEx | Fenix Group International, LLC. | First Call Staffing | Fladger Associates | FleetCor Technologies, Inc. | Flexton | Flywheel Digital | FocusKPI, Inc. | FootBridge Consulting | Forcura | ForgeRock | Forrest Solutions | Fullpower-AI | Further Enterprise Solutions | Gambit Technologies | Gap Inc. | Genuent | GitHub | Good Apple | Gotham Technology Group | Gradient AI | Greater New York Insurance Companies | Greene Resources | Greenphire | Gsquared Group | Guidehouse | HCL America, Inc. | HIRECLOUT | HP | Harnham | Havas Media Group | Hays | Helen of Troy | Hire Talent | Hired by Matrix, Inc | Hiretual | Hirewell | Homesnap | Horizon Media | Horizontal Talent | Hub Recruiting | TA for the Modern World Company Location | Hunter International Recruiting | Hunter Technical | Huxley | IBM | IDR | IDR, Inc. | IQVIA | IRI | ISO | Ibex Investors, LLC. | Ibotta | Idexcel | Illumination Works | Imperfect Foods | Info Junction LLC | Innovative Systems Group | Insider, Inc. | Insight Global | Intellipro Group, Inc. | Intelliswift Software, Inc. | Interactive Resources | International Consulting Associates, Inc. | JCW | JM Eagle | JPI | JRD Systems | Jefferson Frank | Jefferson Wells USA | Jobot | Jobspring Partners | Juristat | KGS Technology Group, Inc | Kairos Living | Kavaliro | KellyMitchell Group, Inc. | Kenshoo | Knowable | Known | Komodo Health | Kung Fu Factory | Kvaliro | LanceSoft, Inc. | Latitude 36, Inc. | Lawrence Harvey | Lawrence Livermore National Laboratory | Ledgent Technology | LevelUP | LexisNexis | Liberty Personnel Services | Life.io | LivePerson | LockerDome | LogistiCare | Lorien | M Science | MEMX | MITRE | Machinify, Inc. | Macro Eyes | Magnifi | Market Street Taleint, Inc. | MaxisIT, Inc. | Maxonic | McKinley Marketing Partners | Media Assembly | Media Storm | MediaMonks | Meredith Corporation | Mesh Recruiting, LLC | Microsoft | Millenium | MindPool, Inc. | Mindlance | Modis | Moodys Northwest Consulting | Motion Recruitment | MotiveMetrics | Mount Indie | Munchkin | NLB Services | Neiman Marcus Group | Next Insurance | NielsenIQ | Novetta | Ntelicor | Nvidia | OMD USA | Odyssey Information Services | OkCupid | Olive | Omni Tax Help | On The Hook Recruiting, LLC. | OneWire | Onebridge | Open Systems Inc. | Open Systems Technologies | OpenArc, LLC. | Optello | Optomi | PCR Staffing | PRI Technology | Pangaea | Parker and Lynch | Paro.io | Patel Consulatants | Pathrise | PayPal | Pernod Ricard | Peterson Technology Partners | Photomath | Pilot Flying J | Piper Companies | Pixalate | Planet Pharma | Plenty | Possible | Precision Technologies Corp. | Primary Services | Prime Team Partners | Priority Payment Systems | Proclinical Ltd. | Prodigy Education | Prometheus Group | Publicis Health Media | Puls | Pyramid Consulting, Inc. | Quadrant Resource | R9 Digital | Radiansys, Inc. | Radley James | Randstad | Rangam Consultants, Inc. | ReMark | Real | RedShred | Rent-A-Center | Reply | Retail Solutions Inc. | RightClick | Rivian | Roblox | Roth Staffing | SBS Creatix, LLC | SNI Financial | SS&C Technologies | Sand Cherry Associates | Sanjay Bharti, MD PLLC | Sauer Brands | Scale Media | Schneider | Scion Staffing | Seagate Technology | Segpay | Selby Jennings | Selling Simplified | Servpro Industries, LLC | ShootProof | SignalPath | Silverpips | SkillSoniq | SkyWater Search Partners | SmartIT Frame | Softworld | Sogeti | Sonder Inc. | Spectrum Reach | StaffSource | Stand 8 Technology Services | States Title | Strategic Employment Partners (SEP) | SurveyMonkey | Susquehanna International Group, LLP | Swift Strategic Solutions Inc. | Synectics Inc. | Synergis | Systecon North America | TM Floyd & Company (TMF) | TRANZACT | TRC Staffing Services, Inc. | Talent 360 Solutions | Tech Observer | TechWorkers | Techni-Tool, Inc. | Technology Ventures | TeePublic | Tencent | The AI Institute | The Equus Group | The Global Edge Consultants | The Home Depot | The Horizon Group | The Jacobson Group | The Judge Group | The Lab Consulting | The Phoenix Group | The Resource Co. | The Select Group | Therabody | Thinaer | Tiger Analytics | Time | Topco Associates LLC | Toyoda Gosei Americas | Tucker Parker Smith Group | US Tech Solutions | Urbint | Ursus, Inc. | VA Boston Healthcare System | Vacasa | Vaco Technology | Valassis | Vans | Vastek, Inc. | Vegamour, Inc. | Verily Life Sciences | Verisk | Via | VideaHealth | Visible | Vivid Resourcing, LTD. | WW (formerly Weight Watchers) | Walgreens | Wanted | White Ops | Wimmer Solutions | Wind River | Wish | Wonderlic | WorldLink US | X-Team | Yelp | Yoh, A Day & Zimmermann Company | Yoh, A Day & Zimmermann Company, LLC | ZenSar | artnet | eCommerce Placement | ettain group | neteffects | s.com | zyBooks | |||
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| 2020-08-11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-08-12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-08-13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-08-14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-08-15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2021-02-12 | 1 | 1 | 2 | 1 | 1 | 1 | 0 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 1 | 1 | 1 | 1 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 7 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 20 | 4 | 1 | 1 | 0 | 1 | 13 | 1 | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 17 | 2 | 1 | 0 | 2 | 1 | 1 | 1 | 5 | 2 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 21 | 2 | 1 | 1 | 1 | 0 | 2 | 1 | 3 | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 15 | 2 | 10 | 2 | 19 | 0 | 1 | 1 | 1 | 1 | 1 | 13 | 2 | 2 | 4 | 12 | 2 | 5 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 8 | 4 | 2 | 4 | 1 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 1 | 21 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 7 | 3 | 1 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 3 | 62 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 6 | 22 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 | 9 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 8 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 8 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 4 | 1 | 1 | 1 |
| 2021-02-13 | 1 | 1 | 2 | 1 | 1 | 1 | 0 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 1 | 1 | 1 | 1 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 7 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 20 | 4 | 1 | 1 | 0 | 1 | 13 | 1 | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 17 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 5 | 2 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 21 | 2 | 1 | 1 | 1 | 0 | 2 | 1 | 3 | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 15 | 2 | 10 | 2 | 19 | 0 | 1 | 1 | 1 | 1 | 1 | 13 | 2 | 2 | 4 | 13 | 2 | 5 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 8 | 4 | 2 | 4 | 1 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 1 | 21 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 7 | 3 | 1 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 3 | 62 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 6 | 22 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 | 9 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 8 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 8 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 5 | 1 | 1 | 1 |
| 2021-02-14 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 1 | 1 | 1 | 1 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 7 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 20 | 5 | 1 | 1 | 1 | 1 | 13 | 1 | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 20 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 5 | 2 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 21 | 2 | 1 | 1 | 1 | 0 | 2 | 1 | 3 | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 15 | 2 | 10 | 2 | 19 | 0 | 1 | 1 | 1 | 1 | 1 | 13 | 2 | 2 | 4 | 13 | 2 | 5 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 8 | 4 | 2 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 1 | 21 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 7 | 3 | 1 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 3 | 62 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 6 | 22 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 9 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 8 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 8 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 6 | 1 | 1 | 1 |
| 2021-02-15 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 1 | 1 | 1 | 1 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 7 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 20 | 6 | 1 | 1 | 1 | 1 | 13 | 1 | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 20 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 5 | 2 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 22 | 2 | 1 | 1 | 1 | 0 | 2 | 1 | 3 | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 15 | 2 | 10 | 2 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 13 | 2 | 2 | 4 | 13 | 2 | 5 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 8 | 4 | 2 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 1 | 21 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 7 | 3 | 1 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 3 | 62 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 6 | 22 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 10 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 8 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 8 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 6 | 1 | 1 | 1 |
| 2021-02-16 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 1 | 1 | 1 | 1 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 7 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 20 | 6 | 1 | 1 | 1 | 1 | 13 | 1 | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 26 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 5 | 2 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 22 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 9 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 15 | 2 | 10 | 2 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 13 | 2 | 2 | 4 | 14 | 2 | 5 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 3 | 2 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 8 | 4 | 2 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 2 | 2 | 1 | 21 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 7 | 3 | 1 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 3 | 64 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 6 | 22 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 10 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 9 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 8 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 6 | 1 | 1 | 1 |
190 rows × 414 columns
state_tally
| State_abbv | State | Count | Text | |
|---|---|---|---|---|
| 0 | CA | California | 237 | 237 applications in California. |
| 1 | NY | New York | 98 | 98 applications in New York. |
| 2 | TX | Texas | 71 | 71 applications in Texas. |
| 3 | WA | Washington | 57 | 57 applications in Washington. |
| 4 | IL | Illinois | 51 | 51 applications in Illinois. |
| 5 | NC | North Carolina | 50 | 50 applications in North Carolina. |
| 6 | MA | Massachusetts | 49 | 49 applications in Massachusetts. |
| 7 | VA | Virginia | 42 | 42 applications in Virginia. |
| 8 | GA | Georgia | 42 | 42 applications in Georgia. |
| 9 | PA | Pennsylvania | 34 | 34 applications in Pennsylvania. |
| 10 | CO | Colorado | 33 | 33 applications in Colorado. |
| 11 | DC | District Of Columbia | 30 | 30 applications in District Of Columbia. |
| 12 | MD | Maryland | 30 | 30 applications in Maryland. |
| 13 | NJ | New Jersey | 24 | 24 applications in New Jersey. |
| 14 | MO | Missouri | 14 | 14 applications in Missouri. |
| 15 | OR | Oregon | 14 | 14 applications in Oregon. |
| 16 | FL | Florida | 13 | 13 applications in Florida. |
| 17 | OH | Ohio | 12 | 12 applications in Ohio. |
| 18 | MN | Minnesota | 12 | 12 applications in Minnesota. |
| 19 | MI | Michigan | 11 | 11 applications in Michigan. |
| 20 | CT | Connecticut | 8 | 8 applications in Connecticut. |
| 21 | AZ | Arizona | 8 | 8 applications in Arizona. |
| 22 | TN | Tennessee | 7 | 7 applications in Tennessee. |
| 23 | KY | Kentucky | 4 | 4 applications in Kentucky. |
| 24 | IN | Indiana | 4 | 4 applications in Indiana. |
| 25 | DE | Delaware | 3 | 3 applications in Delaware. |
| 26 | RI | Rhode Island | 3 | 3 applications in Rhode Island. |
| 27 | SC | South Carolina | 3 | 3 applications in South Carolina. |
| 28 | KS | Kansas | 2 | 2 applications in Kansas. |
| 29 | UT | Utah | 2 | 2 applications in Utah. |
| 30 | WI | Wisconsin | 1 | 1 application in Wisconsin. |
| 31 | AL | Alabama | 1 | 1 application in Alabama. |
| 32 | AR | Arkansas | 1 | 1 application in Arkansas. |
| 33 | ID | Idaho | 1 | 1 application in Idaho. |
| 34 | WV | West Virginia | 1 | 1 application in West Virginia. |